Jeff Zhang

Zesong Jiang, Yuqi Sun, Qing Zhong et al.

Designing optimal Coarse-Grained Reconfigurable Arrays (CGRAs) requires navigating a vast, interdependent hardware/software space bottlenecked by costly manual iteration. We present MACO, an open-source, multi-agent LLM framework that automates CGRA HW/SW co-design. MACO decomposes the design loop into four collaborative stages, HW/SW Co-design, Error Correction, Best-Design Selection, and Evaluation & Feedback, to iteratively optimize power, performance, and area (PPA). To accelerate convergence and efficiently traverse the design space, MACO introduces an exponentially decaying exploration strategy, EDA-guided LLM self-learning, and robust rule-based error correction. Evaluated against state-of-the-art baselines, MACO reduces power consumption by 25.9%, improves performance by 20.0%, and accelerates the search process by 5x. Finally, we validate MACO's physical design through a complete 7nm ASIC design flow.

Yaotian Liu, Sri Sai Rakesh Nakkilla, Xiangyu Zhou et al.

Embodied agents must close a perception-to-action loop on embedded hardware under tight latency, memory, and energy budgets, making deployment a system-level co-design problem rather than a model-accuracy problem. We study this challenge for modular Object Goal Navigation (ObjectNav), where our profiling shows semantic mapping dominates per-step latency while goal prediction dominates peak memory. We formulate edge embodied navigation deployment as a budget-constrained design-space problem and introduce two orthogonal optimization knobs: SKIP, an adaptive sensorimotor scheduler that formalizes safe skipping as a bounded map-impact criterion and learns a lightweight predictor to estimate it from cheap sensor cues at each \texttt{FORWARD} step, exposing a principled quality-efficiency knob (depth-based updates are always retained); and SCOUT, a sparse-context encoder that couples submanifold sparse convolutions on active map regions with a lightweight dense context stream. On HM3D across server and embedded platforms, SKIP+SCOUT delivers up to 1.7x end-to-end speedup, 50.5% lower peak memory, and 7.1% higher SPL than the dense baseline at the selected operating point, outperforming naively smaller perception backbones. SKIP transfers to a second modular pipeline (PONI) with near-lossless performance and remains robust under depth-sensor noise. Together, SKIP+SCOUT expose a family of device-aware Pareto operating points for edge physical AI systems.

Peter Wang, Zhongxia Yan, Jeff Zhang

Video prediction has been an active topic of research in the past few years. Many algorithms focus on pixel-level predictions, which generates results that blur and disintegrate within a few frames. In this project, we use a hierarchical approach for long-term video prediction. We aim at estimating high-level structure in the input frame first, then predict how that structure grows in the future. Finally, we use an image analogy network to recover a realistic image from the predicted structure. Our method is largely adopted from the work by Villegas et al. The method is built with a combination of LSTMs and analogy-based convolutional auto-encoder networks. Additionally, in order to generate more realistic frame predictions, we also adopt adversarial loss. We evaluate our method on the Penn Action dataset, and demonstrate good results on high-level long-term structure prediction.